Dynamic flood modeling essential to assess the coastal impacts of climate change.
Patrick L Barnard, Li H Erikson, Amy C Foxgrover, Juliette A Finzi Hart, Patrick Limber, Andrea C O'Neill, Maarten van Ormondt, Sean Vitousek, Nathan Wood, Maya K Hayden, Jeanne M Jones
Author Information
Patrick L Barnard: United States Geological Survey, Pacific Coastal and Marine Science Center, Santa Cruz, CA, 95060, USA. pbarnard@usgs.gov. ORCID
Li H Erikson: United States Geological Survey, Pacific Coastal and Marine Science Center, Santa Cruz, CA, 95060, USA. ORCID
Amy C Foxgrover: United States Geological Survey, Pacific Coastal and Marine Science Center, Santa Cruz, CA, 95060, USA.
Juliette A Finzi Hart: United States Geological Survey, Pacific Coastal and Marine Science Center, Santa Cruz, CA, 95060, USA. ORCID
Patrick Limber: United States Geological Survey, Pacific Coastal and Marine Science Center, Santa Cruz, CA, 95060, USA. ORCID
Andrea C O'Neill: United States Geological Survey, Pacific Coastal and Marine Science Center, Santa Cruz, CA, 95060, USA. ORCID
Maarten van Ormondt: Deltares, Delft, The Netherlands. ORCID
Sean Vitousek: United States Geological Survey, Pacific Coastal and Marine Science Center, Santa Cruz, CA, 95060, USA. ORCID
Nathan Wood: United States Geological Survey, Western Geographic Science Center, Portland, OR, 97201, USA. ORCID
Maya K Hayden: Point Blue Conservation Science, Petaluma, CA, 94954, USA.
Jeanne M Jones: United States Geological Survey, Western Geographic Science Center, Menlo Park, CA, 94025, USA. ORCID
Coastal inundation due to sea level rise (SLR) is projected to displace hundreds of millions of people worldwide over the next century, creating significant economic, humanitarian, and national-security challenges. However, the majority of previous efforts to characterize potential coastal impacts of climate change have focused primarily on long-term SLR with a static tide level, and have not comprehensively accounted for dynamic physical drivers such as tidal non-linearity, storms, short-term climate variability, erosion response and consequent flooding responses. Here we present a dynamic modeling approach that estimates climate-driven changes in flood-hazard exposure by integrating the effects of SLR, tides, waves, storms, and coastal change (i.e. beach erosion and cliff retreat). We show that for California, USA, the world's 5 largest economy, over $150 billion of property equating to more than 6% of the state's GDP and 600,000 people could be impacted by dynamic flooding by 2100; a three-fold increase in exposed population than if only SLR and a static coastline are considered. The potential for underestimating societal exposure to coastal flooding is greater for smaller SLR scenarios, up to a seven-fold increase in exposed population and economic interests when considering storm conditions in addition to SLR. These results highlight the importance of including climate-change driven dynamic coastal processes and impacts in both short-term hazard mitigation and long-term adaptation planning.
